AI/ML - Data Engineer (NLP/Speech), Siri and Information Intelligence

Apple
Cambridge
8 months ago
Applications closed

Related Jobs

View all jobs

AI / ML Scientist - Biotech

AI/ML, GenAI IT Project Manager

AI Technical Lead, ex .NET C#, Microsoft Developer, AI Maverick Remote

AI Consultant

AI Consultant

AI Consultant

Summary:
Play a part in the next revolution in human-computer interaction. Contribute to a product that is redefining mobile computing. Create groundbreaking technology for large scale systems, natural language, big data, and artificial intelligence. And work with the people who created the intelligent assistant that helps millions of people get things done — just by asking. Join the Siri Response / Text-to-Speech (TTS) team at Apple. Our team is looking for exceptional data engineers passionate about delivering delightful customer experiences with Siri voices. As Data Engineer (NLP/Speech), you'll work on building and maintaining text and speech datasets, processes and workflows for our TTS systems.
Key Qualifications:
5+ years’ industry experience processing large-scale text/speech datasets for ML applicationsStrong expertise in Python, (NoSQL) databases, cloud-based data technologies, and working with large datasets and pipelinesExperience in tooling and streamlining workflows in complex processesHighly-motivated, creative, organized and a strong problem solverOutstanding spoken and written communication skills
Description:
Apple is hiring data engineers for the Siri Response / Text-to-Speech (TTS) team. You'll be working at the frontier of AI, processing massive amounts of speech and text data for our TTS systems. You'll work closely with fellow engineers to gather and integrate new speech and text data into our repositories, transforming raw data into formats usable for TTS model training, and making datasets available to partner teams in Apple to power Siri's voice. Your responsibilities will include: * Collect and centralize data from various sources, working with internal privacy, legal and modeling teams* Build processes and workflows that support data transformation for TTS systems (e.g. audio processing and text annotation), based on the needs and requirements of modeling teams* Provide datasets to partner teams, managing access or usage control* Create dashboard for interactive data exploration* Develop tools and tests to ensure quality and help diagnose issues* Perform analysis on external and internal processes and data to identify opportunities for improvement* Develop prototype ML models utilizing in-house toolkits If this sounds like you, we'd love to hear from you!
Additional Requirements:
* Experience in working with natural language data, lexical resources, corpora, NLP algorithms and tools is a plus* Experience in machine learning, natural language processing, machine translation or text-to-speech is a plus* Knowledge of one or more foreign languages is a plus

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.